Predicting Radiotherapy Patient Outcomes with Real-Time Clinical Data Using Mathematical Modelling.

Autor: Browning AP; Mathematical Institute, University of Oxford, Oxford, UK. browning@maths.ox.ac.uk., Lewin TD; Mathematical Institute, University of Oxford, Oxford, UK.; Roche Pharma Research and Early Development, Roche Innovation Center, Basel, Switzerland., Baker RE; Mathematical Institute, University of Oxford, Oxford, UK., Maini PK; Mathematical Institute, University of Oxford, Oxford, UK., Moros EG; Department of Radiation Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, USA., Caudell J; Department of Radiation Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, USA., Byrne HM; Mathematical Institute, University of Oxford, Oxford, UK., Enderling H; Department of Radiation Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, USA. HEnderling@mdanderson.org.; Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, USA. HEnderling@mdanderson.org.; Department of Radiation Oncology, MD Anderson Cancer Center, Houston, TX, USA. HEnderling@mdanderson.org.
Jazyk: angličtina
Zdroj: Bulletin of mathematical biology [Bull Math Biol] 2024 Jan 18; Vol. 86 (2), pp. 19. Date of Electronic Publication: 2024 Jan 18.
DOI: 10.1007/s11538-023-01246-0
Abstrakt: Longitudinal tumour volume data from head-and-neck cancer patients show that tumours of comparable pre-treatment size and stage may respond very differently to the same radiotherapy fractionation protocol. Mathematical models are often proposed to predict treatment outcome in this context, and have the potential to guide clinical decision-making and inform personalised fractionation protocols. Hindering effective use of models in this context is the sparsity of clinical measurements juxtaposed with the model complexity required to produce the full range of possible patient responses. In this work, we present a compartment model of tumour volume and tumour composition, which, despite relative simplicity, is capable of producing a wide range of patient responses. We then develop novel statistical methodology and leverage a cohort of existing clinical data to produce a predictive model of both tumour volume progression and the associated level of uncertainty that evolves throughout a patient's course of treatment. To capture inter-patient variability, all model parameters are patient specific, with a bootstrap particle filter-like Bayesian approach developed to model a set of training data as prior knowledge. We validate our approach against a subset of unseen data, and demonstrate both the predictive ability of our trained model and its limitations.
(© 2024. The Author(s).)
Databáze: MEDLINE
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